Rangoli Undersampling Library (RUSL) for k-space trajectory design to combine compressed sensing and parallel imaging for accelerated MRI

The current work provides a framework for undersampling patterns for the combination of compressed sensing and parallel imaging. The masks considered for this work are called Rangoli. These masks have pseudo-random distribution of points on Cartesian grid and are smoothly connected. In this work, a variable density spiral was plotted on rangoli and was tweaked by mapping spiral coordinates on to the nearest rangoli coordinates balancing consistency and smoothness error factors to obtain the final trajectory. The images were then evaluated based on the Point spread function (PSF) where PSF was undersampled at different acceleration factors and quantified by peak-signal-to-noise-ratio.

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